Comparative analysis of SVM and k-nearest neighbor classification algorithm in fingerprint detection

An evaluation of SVM and a novel K-Nearest Neighbor classifier for fingerprint verification improvements. Methods and Materials In this article, we compare and contrast the Novel K-Nearest Neighbor Method with a Support Vector Machine. The recommended procedure is applied to a total of 15 samples, 15 from each of the two groups, and the G power is set to 80%. Results: The innovative K-Nearest Neighbor Method method predicts biometrics with an accuracy of 87 percent, which is much higher than the 82 percent accuracy of Support Vector [1]. Here, we calculated a pretest power of 85%, with a sample size of 15 in one group and 30 in total. The mean accuracy for Novel K-Nearest Neighbor (87.12%) and the SVM method (81.85%) are quite similar, however the standard deviation for Novel K-Nearest Neighbor (1.29517) is lower (2.45449) While the Novel K-Nearest Neighbor Method and SVM algorithms achieve similar mean accuracy (87.12 and 81.85 percent, respectively), the standard deviation for the Novel K-Nearest Neighbor Method is somewhat less (1.29517 vs. 2.45449), which is statistically significant at the 0.001 level (p 0.05). The Novel K-Nearest Neighbor Method Algorithm is discovered to be much superior than the SVM algorithm in this research for predicting biometric recognition.